Synthesis of a 2-nitroimidazole derivative N-(4-[18F]fluorobenzyl)-2-(2-nitro-1H-imidazol-1-yl)-acetamide ([18 F]FBNA) as PET radiotracer for imaging tumor hypoxia
Bibliographic record
Abstract
Abstract Background Tissue hypoxia is a pathological condition characterized by reducing oxygen supply. Hypoxia is a hallmark of tumor environment and is commonly observed in many solid tumors. Non-invasive imaging techniques like positron emission tomography (PET) are at the forefront of detecting and monitoring tissue hypoxia changes in vivo. Results We have developed a novel 18F-labeled radiotracer for hypoxia PET imaging based on cytotoxic agent benznidazole. Radiotracer N-(4-[18F]fluorobenzyl)-2-(2-nitro-1H-imidazol-1-yl)acetamide ([18F]FBNA) was synthesized through acylation chemistry with readily available 4-[18F]fluorobenzyl amine. Radiotracer [18F]FBNA was obtained in good radiochemical yields (47.4 ± 5.3%) and high radiochemical purity (> 95%). The total synthesis time was 100 min, including HPLC purification and the molar activity was greater than 40 GBq/µmol. Radiotracer [18F]FBNA was stable in saline and mouse serum for 6 h. [18F]FBNA partition coefficient (logP = 1.05) was found to be more lipophilic than [18F]EF-5 (logP = 0.75), [18F]FMISO (logP = 0.4) and [18F]FAZA (logP = − 0.4). In vitro studies showed that [18F]FBNA accumulates in gastric cancer cell lines AGS and MKN45 under hypoxic conditions. Conclusions Hence, [18F]FBNA represents a novel and easy-to-prepare PET radioligand for imaging hypoxia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".